NO20110886A1 - Attenuation of unwanted acoustic signals with similarity criterion modification - Google Patents

Description

Attenuation of unwanted acoustic signals with similarity criterion modification

FIELD OF THE INVENTION

The present patent description relates to acoustic measurements carried out in a borehole. More specifically, the patent description relates to methods and systems for reducing unwanted signals from acoustic data collected from boreholes.

BACKGROUND OF THE INVENTION

The similarity criterion (eng.: the semblance criterion) is the basis for a widely used method for estimating sonic inertia, especially in connection with P&S logging. See for example C. V. Kimbal and T. Marzetta Semblance Processing of borehole acoustic array data. Geophysics, 49(3):264-281, March 1984 (hereinafter "Kimball 1984"), the contents of which are incorporated by reference. In P&S logging, i.e. monopole logging of compression and cutting with the use of pressure waves, an array-based, non-dispersive processing is used, which is suitable for detecting signals regardless of their energy. This property is essential for the detection of compression arrivals, which are usually weak compared to other arrivals, and for the accurate extraction of their inertia. Therefore, the method has been very successful and used to a large extent.

A side effect of the same property of signal amplitude invariance, however, is that it also responds to very weak events, such as weak tool or casing arrivals, or even reception artifacts. Although such unwanted semblance peaks (eng.: semblance peaks), if they exist, can be handled with many methods, this is a more serious problem for LWD applications where the processing must be carried out down the hole. Although the sonic hardware is designed to attenuate tool arrivals, and further attenuation is possible with advanced processing techniques, there is still a need to be able to avoid such unwanted similarity peaks at tool arrivals.

SUMMARY OF THE INVENTION

In accordance with exemplary embodiments, a method for processing sonic borehole data is proposed. Multichannel sonic data representing sonic energy measured in a borehole is received. The multi-channel data includes data from each of two or more channels. The data from two or more of the channels are combined to generate stacked sonic data. Coherent energy assigned to the stacked sonic data is calculated. Unwanted signals are then attenuated, based at least in part on a comparison between the calculated coherent energy and a predetermined threshold.

In addition, in accordance with some embodiments, a system for processing sonic borehole data is proposed. A storage system is proposed which is adapted and designed for receiving sonic multi-channel data, which represents sonic energy measured in a borehole, the multi-channel data including data from each of two or more channels. A processor is adapted and designed to combine the data from two or more of the channels to thereby generate stacked sonic data, calculate coherent energy associated with the stacked sonic data, and attenuate unwanted signals based at least in part on comparison of the calculated coherent energy with a predetermined threshold.

Further features and advantages will become apparent from the subsequent, more detailed description, and with reference to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in more detail in the following, with reference to the drawings which show non-limiting examples of embodiments of the invention. The same reference numbers are used for the same parts in the various drawings. The drawings show

Fig. 1 a well site system where the invention may be used, in accordance with some embodiments, Fig. 2 shows a sonic LWD (logging-while-drilling) tool, in accordance with some embodiments, Fig. 3 is a plot showing the behavior of various modified similarity criteria, in accordance with some embodiments,

Fig. 4 is a plot showing an inertia estimation error probability distribution,

Fig. 5 is a plot for a case where the threshold is limited to the region of about 60^s/ft for suppressing an LWD weight tube arrival, in accordance with some embodiments, Fig. 6 is a waveform plot showing the synthetic data used for performance comparisons , and Figs. 7a-e are contour plots showing a comparison between the performances of various similarity modification implementations using the synthetic data shown in Figs. 6.

DETAILED DESCRIPTION OF THE PREFERRED

EXECUTION FORMS

In the more detailed description of the preferred embodiments given below, references are made to the various drawings. The drawings form part of the description, and show by way of illustration possible embodiments for practicing the invention. However, it is understood here that the use of other designs and structural changes is conceivable, without thereby going outside the scope of the invention.

The details described herein are intended only as examples, and to illustrate the invention, and are provided to describe what is believed to be the best and most easily understood description of principles and conceptual aspects of the invention. Structural details of the invention are not shown in more detail than is considered necessary for the basic understanding of the invention. The description and drawings are considered to show professionals how various embodiments of the invention can be implemented in practice. Identical reference numbers and designations in the various drawings indicate identical elements.

In accordance with some embodiments, a given threshold for the received acoustic energy is used to suppress semblance output at weaker arrivals, such as coilar arrivals. This can be carried out via a modification of the similarity criterion (eng.: the semblance criterion). One possible solution is based on interpreting the similarity as a test statistic for detecting coherent arrivals of any energy, as explained below, and modifying this to incorporate the energy threshold requirement. This entails two new modifications, based on implementing the threshold in the detection problem formulation in two different ways, as described in more detail below. In accordance with other embodiments, intuitive and heuristic arguments are used to achieve relatively simple modifications. In accordance with these embodiments, the coherent energy is thresholded, and the energy minimum threshold is subtracted from both coherent and incoherent energy. The performance of the modified criteria is investigated, and different implementations are compared with synthetic data.

Fig. 1 shows a well site system where the invention can be used. The well site can be on land or in the sea. Here, a borehole 11 has been provided in basic formations by means of rotary drilling known per se. Embodiments of the invention may also use directional drilling. This will be described in more detail below.

A drill string 12 hangs in the drill hole 11, and has a bottom hole device 100 with a drill bit 105 at the lower end. The surface system includes platform and crane equipment 10, which is placed above the borehole 11. This equipment 10 includes a rotary table 16, a drive device 17, a hook 18, and a swivel 19. The drill string 12 is rotated by means of the rotary table 16, which is driven by means of means not shown, which affect the drive device 17 at the upper end of the drill string. The drill string 12 hangs in a hook 18 which is connected to a running block (also not shown) via the turning device 17 and a swivel 19, which enables rotation of the drill string in relation to the hook. In a well-known way, it will also be possible to use a top drive system.

In this exemplary embodiment, the surface system further includes drilling fluid or drilling mud 26 which is stored in a pit 27 at the well site. A pump 29 delivers drilling fluid 26 to the interior of the drill string 12, through an opening or gate in the swivel 19, so that the drilling fluid can thereby flow down through the drill string 12, as indicated by arrow 8. The drilling fluid exits the drill string 12 through openings in the drill bit 105, and then circulates up through the annular area between the drill string and the wall of the borehole, as indicated by arrow 9. In this well-known way, the drilling fluid will lubricate the drill bit 105, and carry formation cuttings up to the surface, and to the pit 27 for recirculation.

The downhole device 100 in the illustrated embodiment includes an LWD (logging while drilling) module 120, a MWD (measurement while drilling) module 130, a roto-steerable system, and motor 150, and a drill bit 105.

The LWD module 120 is arranged in a special type of drill weight pipe, in a known manner, and can contain one or more known types of logging tools. It should also be understood here that more than one LWD and/or MWD module can be used, for example as represented by the reference number 120A. (References to a module 120 may alternatively also include a module 120A). The LWD module enables measurement, processing and storage of information, as well as communication with the surface equipment. In present embodiments, the LWD module includes a sonic measuring device. Further, in accordance with some embodiments, various processing steps described herein are performed in a processor disposed in the LWD module 120.

The MWD module 130 is also arranged in a special type of drill weight pipe, in a known manner, and may contain one or more devices for measuring properties in the drill string and the drill bit. The MWD tool further includes a device (not shown) for generating electrical power for the downhole system. This device can typically include a mud turbine generator which is driven by the drilling fluid flow, it being understood here that other power and/or battery systems can also be used.

In the present embodiment, the MWD module includes one or more of the following measuring devices: a weight-on-bit measuring device, a

torque measuring device, a vibration measuring device, a shock measuring device, a slippage measuring device, a direction measuring device, and a

inclination measuring device.

Fig. 2 shows a sonic LWD tool. This can be the LWD tool 120, or it can be part of an LWD tool series 120A of the type described in US patent 6,308,137, certain contents of which are referred to here. In one embodiment, as shown in fig. 2, an offshore rig 210 may be used, and a sonic transmission source or a sonic rate 214 is arranged close to the water surface. Alternatively, any other suitable type of full source or transmitter can be used. For example, in some embodiments, a downhole source 240 may be used. An uphole processor controls the firing of the transmitter 214. The downhole equipment may also include acoustic receivers, and a recorder for reference signals near the source. The uphole equipment also includes telemetry equipment for receiving MWD signals from the downhole equipment. The telemetry equipment and the recorder are typically connected to a processor so that the registrations can be synchronized using uphole and downhole clocks. The downhole LWD module 200 includes at least acoustic receivers 231 and 232, which are connected to a signal processor so that signals detected in the receivers can be recorded synchronously with the firing of the signal source.

The classic similarity criterion proposed by Kimball 1984, and how this is used for the estimation of inertia, will now be described. Given a set of waveforms, xi( t), l=\ L, windows of a certain length Tw are placed at time locations and out-movements given by respectively ro<g>p, and the equality criterion is calculated in the following way for each of these windows.

The out-motion corresponding to the similarity peaks above is then considered to be the inertia of the non-dispersive components of the received data.

For discrete time sampled systems, the integrals in equation (1) are replaced by the sum of corresponding windows:

where Dgter is a time shift operator that shifts the input by 8t (which need not be a multiple of the time sampling period).

This criterion has been investigated and used to a large extent in the treatment of non-dispersive arrivals, as it has been shown to be beneficial for the identification of arrivals independent of the amplitude. See, for example, E. J. Douze and S. J. Laster Statistics of semblance Geophysics, 44(12): 1999-2003, December 1979 (hereafter "Douze 1979"), certain content referred to here.

Signal detection problem

It will now be shown that the similarity criterion is simply the likelihood ratio test statistic for a detection problem (hypothetical testing). To understand this, let us consider the signal detection problem for a case where one observes Ylxn including length Mv traces collected in L receivers, and tries to detect whether in all receivers there is a common (but unknown) signal (transposed to indicate that it is a row vector).

In other words, one has the following hypothetical testing problem:

where 1 is a column vector more just Fere, and is used to indicate that the same signal trace is present in all receivers under the hypotensive Hl. N represents the noise assumed to have a white Gaussian distribution with unknown variance <r<2>. This hypothesis testing problem can be solved by computing the Generalized Likelihood Ratio Test statistic (GLRT) and comparing to a threshold. Harry L. Van Trees Detection, Estimation and Modulation Theory, Part I. Wiley New York, 1968 (hereinafter "Van Trees 1968"), hereby incorporated by reference. The GLRT is obtained by computing the likelihood function under each hypothesis, and taking the ratio of its maximized value for each hypothesis. Note that the likelihood function is obtained with the probability model for the observed data; it is simply the probability density function evaluated at the observed value, expressed as a function of the parameters in the probability model, i.e. that when one has an observable X with a probability density function fx\ ø, from a model parameterized by 6, then one can write like lihood- the function for a given observed value x as L( 6\ x) =fx\ ø( x)). Instead of the likelihood ratio, one can equivalently consider the difference between the logarithms of the likelihood functions, and then get

where LL is the log-likelihood function.

In accordance with some embodiments, one calculates the log-likelihood function under H\ under the assumption of white Gaussian noise, such that

where ||-|| p refers to the Frobenius norm of the argument, and K = ^j—log(27t) is a constant. The quantity in the Frobenius norm can be shown to consist of

where Pi = — Li, V is the projection onto the subspace of 1 while Pr~ is the projection operator for the orthogonal complement of this space.

The log-likelihood in equation (4) can be maximized with respect to the unknown signal s by minimizing the Frobenius norm in equation (5). One can easily see that this is achieved by

put §=■£-!' - Y which means that the second expression in (5) becomes equal to zero.

Finally, the likelihood is maximized with respect to a<2> by setting

using the fact that -«log x - b/ x is maximized when x = b/ n.

If you insert these estimates into the expression for the log-likelihood function in equation (4), you get

where K is the same constant as in equation (4).

By carrying out a similar development for the log-likelihood under Ho, one obtains

with K as the same constant as before.

One can now obtain the GLRT statistics by taking the difference between (6) and (7), and canceling the common expressions:

As before, you note that and therefore that you have where

One observes that the last quantity p has exactly the same form as the equality in equation (2) used in a non-dispersive treatment. Because GLRT is a monotonic function for the similarity p, the latter is considered to be equivalent to the former, for the detection of a signal present in all sensors.

One can therefore interpret the inertia processing methodology as running a detector for each of the number of time-window locations, and out-movements, and estimating the inertia of propagating non-dispersive components as the out-movement values where the detector output shows a local peak.

It is noted that the similarity criterion is invariant for any scaling of the data, and can therefore be used in the detection of weak arrivals, such as compressions, even at strongly varying amplitudes. The similarity criterion is therefore used to a large extent in the commercial processing.

Proposal for modification of similarity

We now consider the situation (especially for LWD) due to the same scale invariance, namely that in some cases weak, unwanted arrivals such as tools or casing (even after attenuation steps in the hardware and/or preprocessing) could be registered as high similarity events, and thereby masking or interfering with the downhole processing of the true arrivals.

Using the teachings provided in the foregoing text, one can address this situation by requiring a minimum energy threshold for the signal to be detected. We look here at two different ways of incorporating this requirement into the signal detection problem, deriving suitable modifications in each case with respect to the detection test statistics, and therefore the similarity in the following two subsections.

Detection of signal above the threshold

In accordance with some embodiments, the detection problem is considered a hypothesis testing problem as before, but with the additional requirement that the signal present during H\satisfies a certain threshold with respect to its energy (or amplitude):

where we have now placed a threshold £ on the energy of the unknown signal.

The maximized log-likelihood under H0 is identical to that in the previous section. We therefore look at the quantity under H\. As before, one calculates

We first maximize this, taking into account the following restrictions on the signal norm

Focusing on the expression containing s', one tries to minimize this in order to thereby maximize the log likelihood, and one finds that the minimization argument under the constraint is given by

as the signal amplitude estimate is given by It can be shown that the quadratic expression in brackets in equation (1) will be equal to The second expression (times L) is further simplified after a certain calculation to By repeating the same steps as in the previous section, GLRT - the statistics for this problem as where an analogous (modified) similarity criterion has been defined

with v as defined in (13).

Rejection of signal below the threshold

In accordance with some embodiments, a more general scenario is used where the threshold below Ho is also used. In other words, one expects that there may be a signal below a threshold, but that it is interpreted as a false arrival instead of the desired signal. Naturally, one must also have a threshold to indicate the presence of the signal, and this threshold must be greater than the former.

In other words, one considers the detection problem as before, but with the following modifications:

where we now consider that the signal energy satisfies the thresholds € o and e\ under Ho respectively H\ with e\ > eo. the maximization of the log likelihood under H\ is exactly the same as in the previous section, with e\ replacing e in the corresponding expressions. The maximization under Ho is now also similar to that for H\, but with the difference that the estimate for the signal amplitude is given by

instead of the max function for equation (12).

Working through the remaining steps, one finds that one can express the GLRT statistic as

This can again be put in an equality-like form (with a certain amount of work) as in front of where with

Behavior

We now look at the behavior of the new similarity criteria according to the embodiments described above. One first notes that the modified criterion for equation (15) is equal to the standard similarity quantity when the signal estimate exceeds the threshold

and that there is absolutely no difference from the standard output. However, when the signal norm is below the threshold, the similarity will fall rapidly towards zero, and become equal to that at half the threshold value. Below this, the similarity will become negative, but can be ignored as it is now implied that the presence of a signal above the desired threshold is very unlikely, and in practice one saturates it at zero.

Simple threshold similarity

The foregoing property of a likelihood ratio detector motivates a much simpler modification of the similarity. In accordance with some embodiments, one can simply threshold the coherent energy at the particular energy threshold, and calculate the corresponding similarity. where TT is the threshold function:

In other words, one considers the coherent energy only if it exceeds the given threshold during the calculation of the similarity, and this modification is called the threshold similarity. This will clearly be exactly the same as the original equality when the coherent energy exceeds the threshold.

Subtracted similarity

Another modification is inspired by the form of equation (17). In accordance with these embodiments, the energy threshold is subtracted from both the coherent and the total energy in accordance with the rejection of signals below the threshold.

You then get the following form:

where you have thresholded the quantities to keep everything positive, and will have set a small positive minimum of 5 to keep the equality stable (this is usually also used for a standard equality).

Alternatively, one can modify the counter of the similarity quantity by dampening it if it drops below a certain threshold.

The modified equality for equations (17) and (18) is a general expression, which reduces to the individual threshold case in (15) when eo = 0 and to the original equality when e\ = 0. However, it is noted that while we reduce to 1 © Y<2>when the signal estimate above exceeds the threshold s\, the same will apply to vo. Therefore, the modified similarity of equation (17) will never be exactly equal to the original unmodified similarity, but will be smaller or weakened. However, this damping tendency will become small when the signal amplitude rises well above the threshold. The extent of the trend will depend on the value of the original similarity. The modified equality for Equation (20) will have a similar behavior. This point is shown in fig. 3, and described below.

Fig. 3 is a plot showing the behavior of various modified similarity criteria, all in accordance with some exemplary embodiments. The behavior is shown for two cases, in that the signal energy and the total energy are varied, thereby keeping the original equality of p = 1.0 and p = 0.8, respectively. The thresholds in fig. 3 is chosen so that a similarity value of zero is obtained for the same value of the signal amplitude. The modified similarity output from equations (15), (17), (19) and (20) is shown as a function of the signal amplitude for two values of the original similarity (1.0 and 0.8). In particular, curve 330 is the output for equation (15) for an initial equality of 1.0. Curve 334 is the output of equation (17) for the original equality 1.0. Curve 300 is the output of equation (19) for the original equality 1.0. Curve 332 is the output of equation (20) for the original equality of 1.0. Curve 340 is the output for equation (15) for the original equality 0.8. Curve 344 is the output for equation (17) for the original equality 0.8. Curve 322 is the output for equation (19) for the original equality 0.8. Curve 342 is the output of equation (20) for the original equality 0.8. As mentioned, the thresholds for each case have been chosen for aligning the zero cutoff for each modified criterion. In particular, the threshold for the execution of equation (15) is e = 2; the thresholds for the executions of equation (17) are so = 0.5 and S\= 1.5; the thresholds for the realizations of equations (19) and (20) are 1. It can be observed that while "LR"

(equation (15), curves 330 and 340) and "Thr" (equation (19), curves 320 and 322) criteria return the original similarity above the threshold. The other two (equation (17), curves 334 and 344; and equation (20), curves 332 and 342) show a tendency especially when the original similarity is below 1. However, this tendency will quickly become small when the signal amplitude increases, and for values well above the threshold, the difference relative to the original will be small.

Signal detection performance is investigated based on Monte Carlo simulations, using 10,000 trials using signal + noise, and noise only data to estimate the probability of detection (PD) for a given probability of a false alarm (PFA) of 0.01. This was repeated for a number of signal levels to keep the noise level fixed. It was found that embodiments described with respect to equations (15), (17), (19) and (20) are all effective. In particular, while the original, non-thresholded similarity detects signals below the desired level, the modified criteria will successfully discriminate such cases.

The inertia estimation accuracy has been evaluated using the modified criteria, and compared with that of the original unthresholded similarity. Monte Carlo simulations were again run to tabulate the deviation for the equality inertia relative to the true value. Fig. 4 is a plot showing the inertia estimation error probability distribution. Plot 410 shows the inertia estimation error probability distribution at an SNR of 5 dB for the original, and for each of the modified criteria. From fig. 4, it can be seen that the error distribution is almost identical for all cases, and that the inertial measurement is not disturbed by using the modifications for suppressing the weak signals.

According to some embodiments, the impact of the threshold can be further minimized by adapting it to the inertial time range where the unwanted arrivals are expected. If, for example, the unwanted signal is a weak throat tube arrival, then one will be able to feel the approximate inertia of this, and can adapt the threshold around it. The signal energy threshold is given as a function of the inertia, thereby applying close to the expected tube inertia. A similar adjustment can be made around the expected arrival time of the signal. Fig. 5 shows a plot 510 for a case where the threshold is limited to the range of about 60 s/ft, for suppression of an LWD weight tube arrival. In accordance with other embodiments, if one can also predict the arrival time, one can similarly limit the threshold also in the time domain.

Results comparison

One now compares the performance of the modified similarity criteria with the original one, starting with a synthetic example. A synthetic waveform was created which had two components with outward movements of 60^s/ft and 80^s/ft respectively. The first component is considered to be a weak and unwanted arrival (amplitude = 1), such as the collar, while the second component is the desired signal, such as the compression (amplitude = 5). Noise was added according to an SNR of 20 dB.

Fig. 6 is a waveform plot showing the synthetic data used for performance comparisons. The synthetic data shown in Fig. 6, shows two arrivals: a "tool" arrival 610 with an amplitude of 1, and a "compression" arrival 612 with an amplitude of 5. Figs. 7a-e are contour plots, showing a comparison between the performances of different similarity modification implementations using the synthetic data shown in Fig. 6. Fig. 7 shows the contour plot of the original similarity in the STC plane. One sees the presence of two arrivals, namely the arrival 710 which corresponds to the tool arrival 610 in FIG. 6, and the arrival 712 corresponding to the compression arrival 612 in FIG. 6. Fig. 7b shows a contour plot of the first modified criterion, in accordance with equation 15 and with a threshold of 2. In fig. 7b, the arrival 714 corresponds to the component arrival 612. From fig. 7b, it can also be seen that the first unwanted arrival, the tool arrival 610 in FIG. 6, is effectively suppressed. Interestingly, the long tail of the STC contours for arrival 714 is also truncated as the threshold acts to remove the contribution from the second component's weak tail. Fig. 7c shows a contour plot for the second modified criterion, according to equation (17), with thresholds 0.5 and 1.5, and the arrival 716 corresponding to the compression arrival 612 in Fig. 6. Fig. 7d shows a contour plot of the modified similarity obtained by subtracting the threshold from the coherent energy and the total energy, according to equation (19). The arrival 718 corresponds to the compression arrival 612 in FIG. 6. The contours are sharper and show less similarity consistency with the behavior in fig. 3, curves 332 and 342. Finally, fig. 7e a contour plot of the threshold similarity, which is simply a masked portion of the original similarity contour, but retaining peaks of interest, according to equation (20). The arrival 720 corresponds to the compression arrival 612 in fig. 6. Note that all the cases shown in fig. 7b-e, the estimated inertia is very close up to the true value of 80.

The modified similarity criterion has also been evaluated on LWD field data at a location where the formation is firm, and where the compression arrival is closer to the tool arrival. It is seen that the esophageal arrival, which appeared after the conventional similarity treatment, is effectively removed using each of the modified similarity criteria without any effect on the main arrival.

Many changes and modifications of the invention will no doubt be understood by a person skilled in the art after a study of the preceding description, and it should be understood here that the embodiments shown and described are in no way intended to be limiting. Furthermore, the invention is described in connection with particular preferred embodiments, but variants within the framework of the invention may be possible for professionals. It should be mentioned that the examples are given only to explain the invention, and not to limit it in any way. Although the invention is described in connection with possible examples, it is understood that the words and expressions used here are descriptive words and expressions, and not words or expressions that should have a limiting effect. Changes are conceivable, within the scope of the requirements, as shown and possibly changed, without thereby going outside the scope of the invention. Although the invention is described in connection with special means, materials and designs, the invention is not intended to be limited to what is shown and described. On the contrary, the invention is considered to cover all functionally equivalent structures, methods and uses, all within the scope of the invention as determined by the patent claims.

Claims (27)

1. Method of processing borehole sonic data, including: receiving multichannel sonic data representing sonic energy measured in a borehole, said multichannel data including data from each of two or more channels, combining the data from two or more channels to generate stacked sonic data , calculating coherent energy associated with the stacked sonic data, and suppressing unwanted signals based at least in part on the calculated coherent energy. 2. Method according to claim 1, characterized in that the attenuation of unwanted signals is based at least in part on a comparison of the calculated coherent energy with a predetermined threshold. 3. Method according to claim 2, characterized in that the unwanted signals are removed in cases where the calculated coherent energy is less than the predetermined threshold. 4. Method according to claim 1, characterized in that it further includes calculation of similarity values based on the coherent energy, the similarity values being attenuated in cases when the calculated coherent energy is less than the predetermined threshold. 5. Method according to claim 1, characterized in that it further includes: calculation of a probability function for a criterion to determine whether the signal should be attenuated or not, and calculation of similarity values (eng.: semblance values) based on the calculation of the probability function and the calculated coherent energy, the similarity values being attenuated based on the calculated probability function. 6. Method according to claim 5, characterized in that the probability function includes a likelihood function or a log-likelihood function. 7. Method according to claim 5, characterized in that the probability function corresponds to the detection of signals above a predetermined threshold energy. 8. Method according to claim 5, characterized in that the probability function corresponds to the rejection of signals below a predetermined threshold energy. 9. Method according to claim 2, characterized in that the predetermined threshold is a fixed value. 10. Method according to claim 2, characterized in that the predetermined threshold is a function of a parameter associated with the sonic data. 11. Method according to claim 10, characterized in that the predetermined threshold is a function of inertia and/or time to apply to an expected signal type. 12. Method according to claim 11, characterized in that the signal type is an unwanted utility propagated signal or a casing arrival. 13. Method according to claim 11, characterized in that the signal type is a compression signal arrival of interest. 14. Method according to claim 1, characterized in that the sonic multi-channel data is measured during a drilling operation using a number of sonic receivers mounted on a drill weight pipe body. 15. Method according to claim 14, characterized in that it is carried out using a processing system which is arranged in the drill weight pipe body. 16. Method according to claim 1, characterized in that the sonic multi-channel data is measured with a wire tool, which has at least one sonic source, and a number of sonic receivers mounted on the tool. 17. System for processing borehole sonic data, including: a storage system adapted and designed to receive multichannel sonic data representing sonic energy measured in a borehole, which multichannel data includes data from each of two or more channels, and a processor adapted and configured to combine of the data from two or more of the channels, to generate stacked sonic data, calculate coherent energy associated with the stacked sonic data, and suppress unwanted signals based at least in part on a comparison of the calculated coherent energy with a predetermined threshold. 18. System according to claim 17, characterized in that it further includes a tool body which is suitable for placement down in the hole, as the storage system and the processor are arranged in the tool body. 19. System according to claim 18, characterized by the number of downhole sonic receivers mounted on a borehole pipe, and adapted for measuring the sonic multi-channel energy, the tool body being placed on the borehole pipe, and the storage system recording the sonic measurements from the sonic receivers. 20. System according to claim 17, characterized in that the sonic multichannel data is measured with a wire tool that has at least one sonic source, and a number of sonic receivers mounted on the tool, the processing system being placed on the surface. 21. System according to claim 17, characterized in that the processor is further adapted and designed for calculating similarity values based on the calculated coherent energy, the similarity values being attenuated in cases where the calculated coherent energy is less than the predetermined threshold. 22. System according to claim 17, characterized in that the processor is further adapted and designed for calculating a probability function for a criterion, to determine whether the signal should be attenuated or not, and to calculate similarity values based on the calculation of the probability function and the calculated coherent energy, the similarity values being attenuated based on the calculated the probability function. 23. System according to claim 22, characterized in that the probability function includes a likelihood function or a log-likelihood function. 24. System according to claim 22, characterized in that the probability function corresponds to the detection of signals above a predetermined threshold energy. 25. System according to claim 22, characterized in that the probability function corresponds to the rejection of signals below a predetermined threshold energy. 26. System according to claim 17, characterized in that the predetermined threshold is a function of a parameter assigned to the sonic data. 27. System according to claim 26, characterized in that the predetermined threshold is a function of inertia and/or time, to apply to an expected type of unwanted signal.